Automatic identification method for fraudulent users on e-commerce websites based on Random Forest algorithm

Abstract

With the rapid development of e-commerce, online fraud is increasing, seriously threatening the interests of users and the reputation of e-commerce platforms. In order to effectively identify and prevent fraudulent users on e-commerce websites, a research proposes an automatic identification method on the basis of Random Forest algorithm. It achieves automatic detection of fraudulent behavior by analyzing the behavioral characteristics and user activity patterns of the website. The research results demonstrated that the accuracy on the experimental dataset is as high as 98.76%, with a recall rate of 97.53%, which is much higher than other methods and demonstrates extremely high recognition performance. This method can also effectively identify different types of fraudulent behavior, with a recognition rate of over 98%. This result further confirms the advantages of the Random Forest algorithm in dealing with imbalanced datasets and complex classification problems. Research can help combat online fraud and promote the healthy development of the e-commerce industry.